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---
title: ML Pipeline for Cybersecurity Purple Teaming
emoji: 🛡️
colorFrom: red
colorTo: blue
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: mit
---
# ML Pipeline for Cybersecurity Purple Teaming 🛡️
A scalable Streamlit-based machine learning pipeline platform specialized for cybersecurity purple-teaming, enabling advanced data processing and model training.
[](https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline)
## Features 🚀
- **Distributed Data Processing**: Leverage Dask for handling large-scale datasets
- **Interactive ML Pipeline**: Build and customize machine learning workflows
- **Real-time Visualization**: Monitor model performance and data insights
- **Cybersecurity Focus**: Tailored for purple team operations and security analytics
## Tech Stack 💻
- **Dask**: Distributed data processing
- **Scikit-learn**: ML model training and evaluation
- **Streamlit**: Interactive web interface
- **Pandas/NumPy**: Data manipulation and analysis
- **Matplotlib/Seaborn**: Data visualization
## Getting Started 🏁
1. Visit the [Space on Hugging Face Hub](https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline)
2. Upload your cybersecurity dataset (CSV/JSON format)
3. Configure the ML pipeline parameters
4. Train and evaluate your model
5. Export the trained model for deployment
## Usage Guide 📖
1. **Data Upload**
- Support for CSV and JSON formats
- Automatic handling of large datasets using Dask
2. **Pipeline Configuration**
- Choose preprocessing steps
- Configure model parameters
- Select features for training
3. **Model Training**
- Interactive parameter tuning
- Real-time performance metrics
- Visual model evaluation
## Local Development
1. **Clone the repository**
```bash
git clone https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline
cd cybersec-ml-pipeline
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Run the application**
```bash
streamlit run app.py
```
## Contributing 🤝
Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests.
## License 📄
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments 👏
- Streamlit community for the amazing framework
- Scikit-learn team for the ML tools
- All contributors who help improve this project |